code: def test_quantize_openvino_with_tuning(self):
      trainer = Trainer()
      model = mobilenet_v3_small(num_classes=10)
  
      x = torch.rand((10, 3, 256, 256))
      y = torch.ones((10,), dtype=torch.long)
  
      ds = TensorDataset(x, y)
      dataloader = DataLoader(ds, batch_size=2)
  
      optimized_model = InferenceOptimizer.quantize(
          model,
          accelerator="openvino",
          calib_data=dataloader,
          metric=F1Score("multiclass", num_classes=10),
      )
  
      y_hat = optimized_model(x[0:3])
      assert y_hat.shape == (3, 10)
      y_hat = optimized_model(x)
      assert y_hat.shape == (10, 10)
  
      trainer.validate(optimized_model, dataloader)
      trainer.test(optimized_model, dataloader)
      trainer.predict(optimized_model, dataloader)
  
imports: from unittest import TestCase
  from torchmetrics import F1Score
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.mobilenetv3 import mobilenet_v3_small
  from torchvision.models import resnet50
  import torch
  from torch import nn
  from torch.utils.data.dataset import TensorDataset
  from torch.utils.data.dataloader import DataLoader
  import tempfile
  import pytest
  import numpy
label_desc: Use image node_id 2 for calling the TestOpenVINO obj's underlying member method code with example usage: obj.test_quantize_openvino_with_tuning() without return types